System architecture and edge tracking performance of an automated robotic deburring workcell
Bibliographic record
Abstract
This paper presents the system architecture and performance of a robotic workcell capable of deburring used components. Such a workcell should possess the ability to automatically probe, reconstruct the surface geometry, determine the tool path for further machining, and finally carry out edge finishing. The robot chosen is YAMAHA Zeta-1 robot designed specifically for deburring. Probing is accomplished by means of a displacement sensor. This obviates the need for an additional probing stage in the process. The workcell controller has an information processing part consisting of a PC-parallel processor network, an interface to communicate with the controller of the deburring robot and a hardware interface for the probe. The surface of the workpiece is probed by the robot, directed along a path prescribed by the PC-parallel processor network, based on the original CAD database. The probed points, thus collected are used to reconstruct the surface probed and extrapolate to obtain the edge profile. The tool path constructed from the edge profile is then used to direct the robot during machining. Results of edge tracking of a saddle surface in three dimensions is presented. The geometric tacking error is found to be within the resolution of the command signals (0.063 mm).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".